Multi-Feature Integration with Relevance Feedback on 3D Model Similarity Retrieval

Saiful Akbar; Ary Setijadi Prihatmanto; Roland Wagner; Josef Küng

Multi-Feature Integration with Relevance Feedback on 3D Model Similarity Retrieval

Saiful Akbar, Ary Setijadi Prihatmanto, Roland Wagner, Josef Küng

Institute for Application-oriented Knowledge Processing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

In this paper, we combine the use of Reduced Feature Vector Integration (RFI) and Distance Integration (DI) with Relevance Feedback (RF) on 3D model similarity retrieval. The RFI outperforms the individual FVs and gives high probability of providing relevant objects, other than the query itself, on the limited-size display window. Therefore, user may select the relevant object(s) just after the initial query. The DI enhances the precision by estimating the weighting factor from the variance of the distance and the rank of relevant objects, and pushing the relevant objects to the top and the irrelevant objects to the bottom. By utilizing both approaches, the small number of RF iterations significantly improves the retrieval precision.

Original language	English
Title of host publication	Proceedings iiWAS 2006, 8th International Conference on Information Integration and Web-based Application & Services
Editors	Gabriele Kotsis, David Taniar, Eric Pardede, Ismail Khalil Ibrahim
Publisher	OCG
Pages	77-86
Number of pages	10
Volume	214
ISBN (Print)	3-85403-214-5
Publication status	Published - Dec 2006

Publication series

Name	[email protected]

Fields of science

102001 Artificial intelligence
102006 Computer supported cooperative work (CSCW)
102010 Database systems
102014 Information design
102015 Information systems
102016 IT security
102028 Knowledge engineering
102019 Machine learning
102022 Software development
102025 Distributed systems
502007 E-commerce
505002 Data protection
506002 E-government
509018 Knowledge management
202007 Computer integrated manufacturing (CIM)
102033 Data mining
102035 Data science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

Akbar, S., Prihatmanto, A. S., Wagner, R., & Küng, J. (2006). Multi-Feature Integration with Relevance Feedback on 3D Model Similarity Retrieval. In Gabriele Kotsis, David Taniar, Eric Pardede, Ismail Khalil Ibrahim (Ed.), Proceedings iiWAS 2006, 8th International Conference on Information Integration and Web-based Application & Services (Vol. 214, pp. 77-86). ([email protected]). OCG.

Akbar, Saiful ; Prihatmanto, Ary Setijadi ; Wagner, Roland et al. / Multi-Feature Integration with Relevance Feedback on 3D Model Similarity Retrieval. Proceedings iiWAS 2006, 8th International Conference on Information Integration and Web-based Application & Services. editor / Gabriele Kotsis, David Taniar, Eric Pardede, Ismail Khalil Ibrahim. Vol. 214 OCG, 2006. pp. 77-86 ([email protected]).

@inproceedings{30d217e4cef64fb1ae98c6730fc00ac3,

title = "Multi-Feature Integration with Relevance Feedback on 3D Model Similarity Retrieval",

abstract = "In this paper, we combine the use of Reduced Feature Vector Integration (RFI) and Distance Integration (DI) with Relevance Feedback (RF) on 3D model similarity retrieval. The RFI outperforms the individual FVs and gives high probability of providing relevant objects, other than the query itself, on the limited-size display window. Therefore, user may select the relevant object(s) just after the initial query. The DI enhances the precision by estimating the weighting factor from the variance of the distance and the rank of relevant objects, and pushing the relevant objects to the top and the irrelevant objects to the bottom. By utilizing both approaches, the small number of RF iterations significantly improves the retrieval precision.",

author = "Saiful Akbar and Prihatmanto, {Ary Setijadi} and Roland Wagner and Josef K{\"u}ng",

year = "2006",

month = dec,

language = "English",

isbn = "3-85403-214-5",

volume = "214",

series = "[email protected]",

publisher = "OCG",

pages = "77--86",

editor = "{Gabriele Kotsis, David Taniar, Eric Pardede, Ismail Khalil Ibrahim}",

booktitle = "Proceedings iiWAS 2006, 8th International Conference on Information Integration and Web-based Application & Services",

}

Akbar, S, Prihatmanto, AS, Wagner, R & Küng, J 2006, Multi-Feature Integration with Relevance Feedback on 3D Model Similarity Retrieval. in Gabriele Kotsis, David Taniar, Eric Pardede, Ismail Khalil Ibrahim (ed.), Proceedings iiWAS 2006, 8th International Conference on Information Integration and Web-based Application & Services. vol. 214, [email protected], OCG, pp. 77-86.

Multi-Feature Integration with Relevance Feedback on 3D Model Similarity Retrieval. / Akbar, Saiful; Prihatmanto, Ary Setijadi; Wagner, Roland et al.
Proceedings iiWAS 2006, 8th International Conference on Information Integration and Web-based Application & Services. ed. / Gabriele Kotsis, David Taniar, Eric Pardede, Ismail Khalil Ibrahim. Vol. 214 OCG, 2006. p. 77-86 ([email protected]).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - Multi-Feature Integration with Relevance Feedback on 3D Model Similarity Retrieval

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AU - Prihatmanto, Ary Setijadi

AU - Wagner, Roland

AU - Küng, Josef

PY - 2006/12

Y1 - 2006/12

N2 - In this paper, we combine the use of Reduced Feature Vector Integration (RFI) and Distance Integration (DI) with Relevance Feedback (RF) on 3D model similarity retrieval. The RFI outperforms the individual FVs and gives high probability of providing relevant objects, other than the query itself, on the limited-size display window. Therefore, user may select the relevant object(s) just after the initial query. The DI enhances the precision by estimating the weighting factor from the variance of the distance and the rank of relevant objects, and pushing the relevant objects to the top and the irrelevant objects to the bottom. By utilizing both approaches, the small number of RF iterations significantly improves the retrieval precision.

AB - In this paper, we combine the use of Reduced Feature Vector Integration (RFI) and Distance Integration (DI) with Relevance Feedback (RF) on 3D model similarity retrieval. The RFI outperforms the individual FVs and gives high probability of providing relevant objects, other than the query itself, on the limited-size display window. Therefore, user may select the relevant object(s) just after the initial query. The DI enhances the precision by estimating the weighting factor from the variance of the distance and the rank of relevant objects, and pushing the relevant objects to the top and the irrelevant objects to the bottom. By utilizing both approaches, the small number of RF iterations significantly improves the retrieval precision.

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M3 - Conference proceedings

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BT - Proceedings iiWAS 2006, 8th International Conference on Information Integration and Web-based Application & Services

A2 - Gabriele Kotsis, David Taniar, Eric Pardede, Ismail Khalil Ibrahim, null

PB - OCG

ER -

Akbar S, Prihatmanto AS, Wagner R , Küng J. Multi-Feature Integration with Relevance Feedback on 3D Model Similarity Retrieval. In Gabriele Kotsis, David Taniar, Eric Pardede, Ismail Khalil Ibrahim, editor, Proceedings iiWAS 2006, 8th International Conference on Information Integration and Web-based Application & Services. Vol. 214. OCG. 2006. p. 77-86. ([email protected]).

Multi-Feature Integration with Relevance Feedback on 3D Model Similarity Retrieval

Abstract

Publication series

Fields of science

UN SDGs

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Cite this